جستجوی مقالات مرتبط با کلیدواژه « tm sensor » در نشریات گروه « جغرافیا »

The purpose of this study was to estimate the level of deforestation in Astara county over a 24 year period and to prioritize the effective factors on deforestation in the area. For this purpose, the forest cover map of the study area for the years 1987 and 2011 was prepared and compared using Landsat 8 satellite TM images. In this study, 100 training samples for two classes of forest and non-forest were used and the images were classified according to the maximum probability method. The physiographic factors map of slope, direction, elevation and human factors of distance from road and distance from residential areas were also obtained using digital elevation model (DEM) and related objects map. Existing maps were matched and zoning map of destruction risk of the area prepared in GIS environment and finally correlation between levels of destruction and factors affecting the destruction were taken. The results showed that the forest area of the study area decreased over the 5744 hectares during 24 years. Distance from residential areas and distance from the roads were had the most correlation with the level of destruction. The direction and altitude factors had no significant relationship with degradation but slope had a moderate effect on degradation. As a result, human factors were identified as the main causes of deforestation in the region. Therefore, the restoration and protection of forests in the region should be prioritized through the control of human-made areas.

In recent decades remote sensing-based forest stands biomass estimation techniques have a great importance. In this study, the Above-Ground Biomass (AGB) of Iranian northern beech forests was estimated by TM sensor of Landsat 5 satellite. Required Pre-processing and processing tasks was carried out on the images. For estimation of above-ground biomass, 65 sample plots with dimensions of 45m × 45m were laid out in the field. In each sample plot, diameter at the breast height (DBH) of trees higher than 7.5cm was measured and consequently, the above-ground biomass was calculated for each sample plot. 45 and 20 sample plots were considered for modelling and validation processes, respectively. Parametric multivariate linear regression was used for modelling. Pearson correlation between above-ground biomass in sample plots and correspond spectral values in calculated and original bands showed that the near infra-red band (band 4) was most correlated with above-ground biomass at 99% confidence level and correlation coefficient of 0.427. Implementing of stepwise multivariate linear regression method between above-ground biomass and all other remotely sensed variables revealed that the AGB= 6/682b4 – 206/693 model with adjusted R square=0.164 and RMSE=15/4 % is the best model (similar to simple linear regression between AGB and NIR band) for estimation of Iranian northern beech forests above-ground biomass in studied area. Conclusively, this approach is able to estimate woody biomass in pure beech stands relatively good especially in small scales.

In recent years, the zone one of Shiraz city has experienced extreme changes because the quantity and quality of vegetation suffered greatly from the inattention it is to be noted that the abundance of vegetation is one of the most important factors affecting the land surface temperature in this study, the thermal characteristics of NDVI and its relationship to the urban environment were examined using multi-temporal Landsat Satellite images. To do so, four TM sensor images on 22 March 2009, 15 July 2010, 19 October 2010, and 22 November 2010 were received, and then land surface temperature (LST) and normalized vegetation difference index were calculated. Results of the study showed that the highest percentage of vegetation, regardless of that in winter and spring, is associated with the lowest land surface temperature. The highest temperature in all seasons were reported in the heights towards the south, southeast and barren areas. Also, in spring the highest level of vegetation index had no local conformity with the lowest temperature in other words, the vegetation index and land surface temperature had no inverse relationship. In summer, the highest percent of vegetation of land surface temperature of the spatial was related to the lowest spatial adaptation. In winter, the distributions of temporal patterns were quite different from those in other seasons because of moderating role of temperature in vegetation. The highest range of temperature was observed at the highest elevations as well as in areas with use of vegetation. In general, it can be concluded that the frequency and quality of vegetation are important factors which affect the land surface temperature.

Soil salinity and salinization of lands, as one of the main problems of agriculture, has paramount importance and can be avoided with proper understanding of its progress. This is the first step in identifying areas of salt and salt mapping in these soils. With the development of remote sensing technology and efficient use of satellite imaging, this study compared the salinity maps produced by a variety of classification image algorithms (Maximum likelihood, Minimum distance and Parallelepiped) by Landsat5 TM satellite data in the East of Khoy. In this study, 269 soil samples were analyzed and the results obtained were implemented on TM image. For initial identification, topography maps, ENVI 4.8 software and satellite image processing were used and geometric correction was performed with specific locations using GPS. Selected train samples with good distribution in the image and salinity classes were prepared from 1 to 9 determined. Examples of each class of salinity were placed carefully with single pixel size in each image on the corresponding pixel and stored with ROI format, because they had the coordinates. The results indicate the existence of a correlation between bands 1, 4 and 5 TMimage with salinity data and classification algorithms using the Pixel-based method, the highest accuracy of the map is the maximum lkelihood. For this purpose, indicators such as error matrix, Producer’s Accuracy, User’s Accuracy, overall accuracy and kappa Coefficient were extracted. This map is also consistent with field observations of different classes of soil salinity measurement and shows high accuracy of the algorithm among soil salinity maps. The aim of this study was to compare salinity maps prepared by these methods in the area with results of other researchers.

In this study، 14 spectral (vegetation) indices have been calculated from different groups، using spectral bands. The correlation of these vegetation indices with canopy cover has then been determined for three rangeland types and one forest type. The percentage of canopy cover in all vegetation types is measured using step-point method in radial direction (6000 points per vegetation type). The correlation of these vegetation indices with canopy cover is then assessed at the scale of vegetation type and across the vegetation types using SPSS and Erdas Imagine softwares. Different groups of vegetation indices، including slope-based، distance-based، orthogonal transformations and plant–water sensitive ones are extracted from Landsat TM satellite reflectance data and evaluated against field cover data. The results indicated that vegetation indices had higher relationships within vegetation types up to 81% (p<0. 001) but the correlations across vegetation types was decreased due to diversity of vegetation spectra and the effects of background features. Overall، the results indicated that each spectral index was suitable for mapping a specific vegetation type because of structural and biological differences in vegetation cover as well as background soil and these characteristics must be considered for selecting the best indices for mapping and stratification of vegetation types.